langchain-rag

langchain-rag

Pipeline: Index: Load → Split → Embed → Store Retrieve: Query → Embed → Search → Return docs Generate: Docs + Query → LLM → Response Key Components: Document Loaders: Ingest data from files, web, databases Text Splitters: Break documents into chunks Embeddings: Convert text to vectors Vector Stores: Store and search embeddings Vector Store Use Case Persistence InMemory Testing Memory only FAISS Local, high performance Disk Chroma Development Disk Pinecone Production, managed Cloud Complete RAG Pipeline 1. Load documents docs = [ Document(page_content="LangChain is a framework for LLM apps.", metadata={}), Document(page_content="RAG = Retrieval Augmented Generation.", metadata={}), ] 2. Split documents splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50) splits = splitter.split_documents(docs) 3. Create embeddings and store embeddings = OpenAIEmbeddings(model="text-embedding-3-small") vectorstore = InMemoryVectorStore.from_documents(splits, embeddings) 4. Create retriever retriever = vectorstore.as_retriever(search_kwargs={"k": 4}) 5. Use in RAG model = ChatOpenAI(model="gpt-4.1") query = "What is RAG?" relevant_docs = retriever.invoke(query) context = "\n\n".join([doc.page_content for doc in relevant_docs]) response = model.invoke([ {"role": "system", "content": f"Use this context:\n\n{context}"}, {"role": "user", "content": query}, ]) End-to-end RAG pipeline: load documents, split into chunks, embed, store, retrieve, and generate a response. typescript import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai"; import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory"; import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters"; import { Document } from "@langchain/core/documents"; // 1. Load documents const docs = [ new Document({ pageContent: "LangChain is a framework for LLM apps.", metadata: {} }), new Document({ pageContent: "RAG = Retrieval Augmented Generation.", metadata: {} }), ]; // 2. Split documents const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 500, chunkOverlap: 50 }); const splits = await splitter.splitDocuments(docs); // 3. Create embeddings and store const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" }); const vectorstore = await MemoryVectorStore.fromDocuments(splits, embeddings); // 4. Create retriever const retriever = vectorstore.asRetriever({ k: 4 }); // 5. Use in RAG const model = new ChatOpenAI({ model: "gpt-4.1" }); const query = "What is RAG?"; const relevantDocs = await retriever.invoke(query); const context = relevantDocs.map(doc => doc.pageContent).join("\n\n"); const response = await model.invoke([ { role: "system", content: `Use this context:\n\n${context}` }, { role: "user", content: query }, ]); Document Loaders loader = PyPDFLoader("./document.pdf") docs = loader.load() print(f"Loaded {len(docs)} pages") </python> <typescript> Load a PDF file and extract each page as a separate document. typescript import { PDFLoader } from "@langchain/community/document_loaders/fs/pdf"; const loader = new PDFLoader("./document.pdf"); const docs = await loader.load(); console.log(Loaded ${docs.length} pages); loader = WebBaseLoader("https://docs.langchain.com") docs = loader.load() Fetch and parse content from a web URL into a document using Cheerio. typescript import { CheerioWebBaseLoader } from "@langchain/community/document_loaders/web/cheerio"; const loader = new CheerioWebBaseLoader("https://docs.langchain.com"); const docs = await loader.load(); Load all text files from directory loader = DirectoryLoader( "path/to/documents", glob="**/*.txt", # Pattern for files to load loader_cls=TextLoader ) docs = loader.load() </python> </ex-loading-directory> --- ## Text Splitting <ex-text-splitting> <python> Split documents into chunks using RecursiveCharacterTextSplitter with configurable size and overlap. python from langchain_text_splitters import RecursiveCharacterTextSplitter splitter = RecursiveCharacterTextSplitter( chunk_size=1000, # Characters per chunk chunk_overlap=200, # Overlap for context continuity separators=["\n\n", "\n", " ", ""], # Split hierarchy ) splits = splitter.split_documents(docs) Vector Stores vectorstore = Chroma.from_documents( documents=splits, embedding=OpenAIEmbeddings(), persist_directory="./chroma_db", collection_name="my-collection", ) Load existing vectorstore = Chroma( persist_directory="./chroma_db", embedding_function=OpenAIEmbeddings(), collection_name="my-collection", ) Create a Chroma vector store connected to a running Chroma server. typescript import { Chroma } from "@langchain/community/vectorstores/chroma"; import { OpenAIEmbeddings } from "@langchain/openai"; const vectorstore = await Chroma.fromDocuments( splits, new OpenAIEmbeddings(), { collectionName: "my-collection", url: "http://localhost:8000" } ); vectorstore = FAISS.from_documents(splits, embeddings) vectorstore.save_local("./faiss_index") Load (requires allow_dangerous_deserialization) loaded = FAISS.load_local( "./faiss_index", embeddings, allow_dangerous_deserialization=True ) </python> <typescript> Create a FAISS vector store, save it to disk, and reload it. typescript import { FaissStore } from "@langchain/community/vectorstores/faiss"; const vectorstore = await FaissStore.fromDocuments(splits, embeddings); await vectorstore.save("./faiss_index"); const loaded = await FaissStore.load("./faiss_index", embeddings); Retrieval With scores results_with_score = vectorstore.similarity_search_with_score(query, k=5) for doc, score in results_with_score: print(f"Score: {score}, Content: {doc.page_content}") Perform similarity search and retrieve results with relevance scores. typescript // Basic search const results = await vectorstore.similaritySearch(query, 5); // With scores const resultsWithScore = await vectorstore.similaritySearchWithScore(query, 5); for (const [doc, score] of resultsWithScore) { console.log(`Score: ${score}, Content: ${doc.pageContent}`); } Search with filter results = vectorstore.similarity_search( "programming", k=5, filter={"language": "python"} # Only Python docs ) </python> </ex-metadata-filtering> <ex-rag-with-agent> <python> Create an agent that uses RAG as a tool for answering questions. python from langchain.agents import create_agent from langchain.tools import tool @tool def search_docs(query: str) -> str: """Search documentation for relevant information.""" docs = retriever.invoke(query) return "\n\n".join([d.page_content for d in docs]) agent = create_agent( model="gpt-4.1", tools=[search_docs], ) result = agent.invoke({ "messages": [{"role": "user", "content": "How do I create an agent?"}] }) const searchDocs = tool( async (input) => { const docs = await retriever.invoke(input.query); return docs.map(d => d.pageContent).join("\n\n"); }, { name: "search_docs", description: "Search documentation for relevant information.", schema: z.object({ query: z.string() }), } ); const agent = createAgent({ model: "gpt-4.1", tools: [searchDocs], }); const result = await agent.invoke({ messages: [{ role: "user", content: "How do I create an agent?" }], }); ### What You CAN Configure - Chunk size/overlap - Embedding model - Number of results (k) - Metadata filters - Search algorithms: Similarity, MMR ### What You CANNOT Configure - Embedding dimensions (per model) - Mix embeddings from different models in same store Chunk size 500-1500 is typically good. python # WRONG: Too small (loses context) or too large (hits limits) splitter = RecursiveCharacterTextSplitter(chunk_size=50) splitter = RecursiveCharacterTextSplitter(chunk_size=10000) # CORRECT splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) // CORRECT const splitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000, chunkOverlap: 200 }); </typescript> </fix-chunk-size> <fix-chunk-overlap> <python> Use overlap (10-20% of chunk size) to maintain context at boundaries. python # WRONG: No overlap - context breaks at boundaries splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0) # CORRECT: 10-20% overlap splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) CORRECT vectorstore = Chroma.from_documents(docs, embeddings, persist_directory="./chroma_db") Use persistent vector store instead of in-memory to avoid data loss. typescript // WRONG: Memory - lost on restart const vectorstore = await MemoryVectorStore.fromDocuments(docs, embeddings); // CORRECT const vectorstore = await Chroma.fromDocuments(docs, embeddings, { collectionName: "my-collection" }); CORRECT: Same model embeddings = OpenAIEmbeddings(model="text-embedding-3-small") vectorstore = Chroma.from_documents(docs, embeddings) retriever = vectorstore.as_retriever() # Uses same embeddings </python> <typescript> Use the same embedding model for indexing and querying. typescript const embeddings = new OpenAIEmbeddings({ model: "text-embedding-3-small" }); const vectorstore = await Chroma.fromDocuments(docs, embeddings); const retriever = vectorstore.asRetriever(); // Uses same embeddings CORRECT loaded_store = FAISS.load_local("./faiss_index", embeddings, allow_dangerous_deserialization=True) Ensure embedding dimensions match the vector store index dimensions. ```python # WRONG: Index has 1536 dimensions but using 512-dim embeddings pc.create_index(name="idx", dimension=1536, metric="cosine") vectorstore = PineconeVectorStore.from_documents( docs, OpenAIEmbeddings(model="text-embedding-3-small", dimensions=512), index=pc.Index("idx") ) # Error: dimension mismatch! # CORRECT: Match dimensions embeddings = OpenAIEmbeddings() # Default 1536 Weekly Installs1.6KRepositorylangchain-ai/la…n-skillsGitHub Stars376First Seen13 days agoSecurity AuditsGen Agent Trust HubPassSocketWarnSnykWarnInstalled onclaude-code1.4Kcursor1.3Kcodex1.3Kopencode1.2Kgithub-copilot1.2Kgemini-cli1.2K